Interactive Q-Learning for Quantiles

• 成果类型: Article
• 署名作者: Linn, Kristin A.; Laber, Eric B.; Stefanski, Leonard A.
• 署名单位: University of Pennsylvania; North Carolina State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1155993
• 发表日期: 2017
• 页码: 638-649
• 关键词: dynamic treatment regimes; sequenced treatment alternatives; Causal Inference; rationale; DESIGN; models

摘要

A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of-patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms. Supplementary materials for this article are available online.